Update correlation.py

`pos_drown` was a recent change in the autocorr_shape and hadn't been updated in the config yaml file to reflect this. As with all other functions and parameter settings in pyhctsa, this is supposed to be camel case (to match the pythonic naming conventions.

Author: joshuabmoore

pyhctsa/operations/correlation.py

@@ -1742,7 +1742,7 @@ def get_window(step_ind: int):
 
     return np.std(qs, ddof=1)/np.std(y, ddof=1)
 
-def autocorr_shape(y: ArrayLike, stop_when: Union[int, str] = 'posDrown') -> dict:
+def autocorr_shape(y: ArrayLike, stop_when: Union[int, str] = 'pos_drown') -> dict:
     """
     How the autocorrelation function changes with the time lag.
 
@@ -1755,7 +1755,7 @@ def autocorr_shape(y: ArrayLike, stop_when: Union[int, str] = 'posDrown') -> dic
         The input time series.
     stop_when : str or int, optional
         The criterion for the maximum lag to measure the ACF up to.
-        Default is ``'posDrown'``.
+        Default is ``'pos_drown'``.
 
     Returns
     --------
@@ -1777,10 +1777,10 @@ def autocorr_shape(y: ArrayLike, stop_when: Union[int, str] = 'posDrown') -> dic
         acf = autocorr(y, taus, 'Fourier')
         n_drown = stop_when
 
-    elif stop_when in ['posDrown', 'drown', 'doubleDrown']:
+    elif stop_when in ['pos_drown', 'drown', 'double_drown']:
         # Compute ACF up to a given threshold:
         n_drown = 0 # the point at which ACF ~ 0
-        if stop_when == 'posDrown':
+        if stop_when == 'pos_drown':
             # stop when ACF drops below threshold, th
             for i in range(1, N+1):
                 acf_val = autocorr(y, i-1, 'Fourier')[0]
@@ -1811,7 +1811,7 @@ def autocorr_shape(y: ArrayLike, stop_when: Union[int, str] = 'posDrown') -> dic
                     acf.append(acf_val)
                     break
                 acf.append(acf_val)
-        elif stop_when == 'doubleDrown':
+        elif stop_when == 'double_drown':
             # Stop at 2*tau, where tau is the lag where ACF ~ 0 (within 1/sqrt(N) threshold)
             for i in range(1, N+1):
                 acf_val = autocorr(y, i-1, 'Fourier')[0]
@@ -1838,7 +1838,7 @@ def autocorr_shape(y: ArrayLike, stop_when: Union[int, str] = 'posDrown') -> dic
     # Basic stats on the ACF
     out['sumacf'] = np.sum(acf)
     out['meanacf'] = np.mean(acf)
-    if stop_when != 'posDrown':
+    if stop_when != 'pos_drown':
         out['meanabsacf'] = np.mean(np.abs(acf))
         out['sumabsacf'] = np.sum(np.abs(acf))
 
@@ -1879,7 +1879,7 @@ def autocorr_shape(y: ArrayLike, stop_when: Union[int, str] = 'posDrown') -> dic
     fit_success = False
     min_pts_to_fit_exp = 4 # (need at least four points to fit exponential)
 
-    if stop_when == 'posDrown' and nac >= min_pts_to_fit_exp:
+    if stop_when == 'pos_drown' and nac >= min_pts_to_fit_exp:
         # Fit exponential decay to (absolute) ACF:
         # (kind of only makes sense for the first positive period)
         exp_func = lambda x, b : np.exp(-b * x)